The RAdial Velocity Experiment (RAVE): Parameterisation of RAVE spectra based on convolutional neural networks

G. Guiglion*, G. Matijevič, A. B. A. Queiroz, M. Valentini, M. Steinmetz, C. Chiappini, E. K. Grebel, P. J. McMillan, G. Kordopatis, A. Kunder, T. Zwitter, A. Khalatyan, F. Anders, H. Enke, I. Minchev, G. Monari, R. F. G. Wyse, O. Bienaymé, J. Bland-Hawthorn, B. K. GibsonJ. F. Navarro, Q. Parker, W. Reid, G. M. Seabroke, A. Siebert

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    13 Citations (Scopus)
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    Context. Data-driven methods play an increasingly important role in the field of astrophysics In the context of large spectroscopic surveys of stars, data-driven methods are key in deducing physical parameters for millions of spectra in a short time. Convolutional neural networks (CNNs) enable us to connect observables (e.g. spectra, stellar magnitudes) to physical properties (atmospheric parameters, chemical abundances, or labels in general).

    Aims. We test whether it is possible to transfer the labels derived from a high-resolution stellar survey to intermediate-resolution spectra of another survey by using a CNN. 

    Methods. We trained a CNN, adopting stellar atmospheric parameters and chemical abundances from APOGEE DR16 (resolution Ra22 500) data as training set labels. As input, we used parts of the intermediate-resolution RAVE DR6 spectra (R ∼ 7500) overlapping with the APOGEE DR16 data as well as broad-band ALLWISE and 2MASS photometry, together with Gaia DR2 photometry and parallaxes.

    Results. We derived precise atmospheric parameters Teff, log(g), and [M/H], along with the chemical abundances of [Fe/H], [α/M], [Mg/Fe], [Si/Fe], [Al/Fe], and [Ni/Fe] for 420 165 RAVE spectra. The precision typically amounts to 60 K in Teff, 0.06 in log(g) and 0.02-0.04 dex for individual chemical abundances. Incorporating photometry and astrometry as additional constraints substantially improves the results in terms of the accuracy and precision of the derived labels, as long as we operate in those parts of the parameter space that are well-covered by the training sample. Scientific validation confirms the robustness of the CNN results. We provide a catalogue of CNN-Trained atmospheric parameters and abundances along with their uncertainties for 420 165 stars in the RAVE survey.

    Conclusions. CNN-based methods provide a powerful way to combine spectroscopic, photometric, and astrometric data without the need to apply any priors in the form of stellar evolutionary models. The developed procedure can extend the scientific output of RAVE spectra beyond DR6 to ongoing and planned surveys such as Gaia RVS, 4MOST, and WEAVE. We call on the community to place a particular collective emphasis and on efforts to create unbiased training samples for such future spectroscopic surveys.

    Original languageEnglish
    Article numberA168
    Pages (from-to)1-29
    Number of pages29
    JournalAstronomy and Astrophysics
    Publication statusPublished - 16 Dec 2020

    Bibliographical note

    Reproduced with permission from Astronomy & Astrophysics, Copyright ESO 2020. First published in Astronomy and Astrophysics, 644, A168, 2020, published by EDP Sciences. The original publication is available at Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


    • Galaxy: Abundances
    • Galaxy: stellar content
    • Methods: data analysis
    • Stars: Abundances
    • Techniques: spectroscopic


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